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- W3130421866 abstract "Localization of autonomous vehicles in unknown and unstructured GPS-denied environments is still a relevant and major research challenge in the field of Robotics. Applications of such research can be found in search and rescue missions and connected vehicles, where multiple robots need an efficient solution for simultaneous localization through multi-sensor integration so that they can effectively cooperate and coordinate tasks amongst themselves. In this paper, we propose a novel method for estimating the position of a WiFi access point in relation to a moving robot. Specifically, we exploit the integration of two sensors: Direction-of-arrival (DOA) of WiFi signals and the robot’s odometry and combine them with Gaussian probabilistic sampling in a Particle Filter framework. We evaluate the proposed method in terms of accuracy and computational efficiency through extensive trials on datasets gathered from real-world measurements with mobile robots and compared our method against standard approaches. The results demonstrate superior localization accuracy (up to 3x improvement) and capability for most practical applications." @default.
- W3130421866 created "2021-03-01" @default.
- W3130421866 creator A5058010942 @default.
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- W3130421866 date "2020-11-01" @default.
- W3130421866 modified "2023-09-23" @default.
- W3130421866 title "Particle Filter Based Localization of Access Points Using Direction of Arrival on Mobile Robots" @default.
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- W3130421866 doi "https://doi.org/10.1109/vtc2020-fall49728.2020.9348813" @default.
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